4 papers
When and How to Ask: Dynamic Preference Elicitation Strategies for Conversational Recommendation
Feng Xia, Shuo Zhang, Xi Wang
Conversational Recommender Systems (CRSs) are interactive systems that use multi-turn natural language dialogue to understand evolving user preferences and provide personalized rec…
The 2nd EReL@MIR Workshop on Efficient Representation Learning for Multimodal Information Retrieval
Junchen Fu, Xuri Ge, Xin Xin +6
Multimodal representation learning has attracted increasing attention in AI, driven by the strong performance of large, pretrained multimodal foundation models such as Qwen, LLaVA,…
Interplay: Training Independent Simulators for Reference-Free Conversational Recommendation
Jerome Ramos, Feng Xia, Xi Wang +4
Training conversational recommender systems (CRS) requires extensive dialogue data, which is challenging to collect at scale. To address this, researchers have used simulated user-…
DeepRec: Towards a Deep Dive Into the Item Space with Large Language Model Based Recommendation
Bowen Zheng, Xiaolei Wang, Enze Liu +5
Recently, large language models (LLMs) have been introduced into recommender systems (RSs), either to enhance traditional recommendation models (TRMs) or serve as recommendation ba…